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Title:
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ENHANCING LEARNING FROM INFORMATICS TEXTS |
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Author(s):
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Alexandra Gasparinatou , Grammatiki Tsaganou , Maria Grigoriadou |
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ISBN:
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978-972-8924-69-0 |
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Editors:
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Kinshuk, Demetrios G Sampson, J. Michael Spector, Pedro Isaías and Dirk Ifenthaler |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Background knowledge, text coherence, high-knowledge readers, text base and situational understanding, Computer
Networks |
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Type:
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Full Paper |
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First Page:
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245 |
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Last Page:
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252 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Previous studies have demonstrated that high knowledge readers learn more from low-coherence than high-coherence
texts in the domain of Informatics and specifically in the domain of Local Network Topologies. This study explored
deeply the research hypothesis that this characteristic is due to the use of knowledge to fill in the gaps in the text resulting
in an integration of the knowledge of the text with prior knowledge. In the study participants were 65 8th semester
students who had been taught and successfully completed the Data Transmission and Networks Communications
course in the 4th semester of their studies so they are considered high-knowledge readers. Participants comprehension
was examined through free-recall measure, text-based questions, elaborative-inference questions, bridging-inference
questions, problem-solving questions and the sorting task. We found that readers with high background knowledge
performed better after reading the low-coherence text. We support that this happens because the lowcoherence text
forces the readers with high background knowledge to engage in compensatory processing to infer unstated relations in
the text. |
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